@inbook{Alibakhshi:2027:10.1007/978-3-032-35393-1_2,
author = {Alibakhshi, A and Jevsikov, J and Ufumaka, I and Binmadi, R and NG, T and Stowell, CC and Hussain, A and Abdi, A and Serej, ND and Shun-Shin, MJ and Francis, DP and Manisty, CH and Zolgharni, M},
doi = {10.1007/978-3-032-35393-1_2},
pages = {17--30},
title = {Phase-Guided Quality Regression in Apical Echocardiography via CNN-LSTM: A Systematic Backbone Benchmarking Study},
url = {http://dx.doi.org/10.1007/978-3-032-35393-1_2},
year = {2027}
}

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TY  - CHAP
AB  - Apical view acquisition errors in echocardiography can lead to inaccurate estimation of clinically important measurements, including ventricular volume, ejection fraction, and strain. In this work, we propose a phase-centred CNN-LSTM framework for automated regression of apical image quality in echocardiographic studies, investigating which backbone architecture can most effectively predict a continuous reduction in left ventricular long-axis length without relying on explicit mask-based measurements. A patient-specific reference standard was generated by first identifying end-diastole using a phase detection model and then measuring LV long-axis length. For each patient, the scan with the maximum observed LV length served as the patient-specific reference, and deviations across other scans were quantified using a heuristic formulation based on relative length reduction. Using this systematic labelling strategy, eight CNN-LSTM backbone architectures were trained and compared on 1,799 echocardiographic studies from 100 patients, comprising apical four-chamber and apical two-chamber views, with the goal of directly regressing the degree of length reduction from raw image sequences alone. Across backbone comparisons, convolution-based models showed the strongest overall performance, with EfficientNetV2 achieving the lowest regression error for apical four-chamber views and ConvNeXt-Tiny and ResNet50-level models showing competitive performance in apical two-chamber views. To assess clinical relevance, two expert-annotated evaluation datasets, each labelled by 10 experts, were used for ranking-based comparison against expert consensus. The proposed AI approach demonstrated agreement with expert consensus at a level comparable to several human experts and showed meaningful ability to identify scans associated with overestimation and underestimation of key echocardiographic measurements. These findings support the potential of backbone-driven regression of apical view qua
AU  - Alibakhshi,A
AU  - Jevsikov,J
AU  - Ufumaka,I
AU  - Binmadi,R
AU  - NG,T
AU  - Stowell,CC
AU  - Hussain,A
AU  - Abdi,A
AU  - Serej,ND
AU  - Shun-Shin,MJ
AU  - Francis,DP
AU  - Manisty,CH
AU  - Zolgharni,M
DO  - 10.1007/978-3-032-35393-1_2
EP  - 30
PY  - 2027///
SP  - 17
TI  - Phase-Guided Quality Regression in Apical Echocardiography via CNN-LSTM: A Systematic Backbone Benchmarking Study
UR  - http://dx.doi.org/10.1007/978-3-032-35393-1_2
ER  - 

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